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Databases · head to head

DynamoDB vs Upstash

DynamoDB logo

DynamoDB

Databases

AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.

From
Free
Rated
-
Full reviewWebsite
Upstash logo

Upstash

Cloud

Serverless data for modern developers

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: DynamoDB access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.; Upstash hTTP-based REST API only, no TCP connections means higher latency than self-hosted or connection-pooled Redis
  • They diverge on capability: DynamoDB covers Managed and serverless, Upstash covers Serverless Redis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which DynamoDB and Upstash actually diverge.

Attributes where DynamoDB and Upstash differ
AttributeDynamoDBUpstash
Pricing modelusage-basedUnknown
PlatformsAWSREST API, Node.js, Python, Go, Rust, Vercel Edge, Cloudflare Workers, AWS Lambda
CategoryDatabasesCloud
Founded20062020

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in DynamoDB

  • Managed and serverless
  • Predictable latency
  • On-demand or provisioned capacity
  • Global secondary indexes
  • Transactions
  • DynamoDB Streams
  • Global tables
  • Point-in-time recovery

Only in Upstash

  • Serverless Redis
  • Serverless Kafka
  • QStash
  • Global Replication
  • REST API
  • Edge Functions Support
  • Rate Limiting
  • Caching

What people use each for

The jobs each tool is most often brought in to do.

DynamoDB

  • High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot Upstash
  • Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot Upstash
  • Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot Upstash
  • Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot Upstash

Upstash

  • Cachingnot DynamoDB
  • Session storagenot DynamoDB
  • Real-time messagingnot DynamoDB
  • Rate limitingnot DynamoDB
  • Serverless backendsnot DynamoDB

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

DynamoDB

  • Access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.
  • Global secondary indexes are eventually consistent and cannot be read strongly, so a read-after-write against an index can legitimately miss the item that was just written, and application code must be written to tolerate that.
  • Per-partition throughput is capped at roughly 3,000 read and 1,000 write units, so a hot key throttles even when the table has spare capacity overall, and the only real fix is changing the key design to spread the load.
  • Items are limited to 400 KB and query results paginate at 1 MB, so large or list-shaped data has to be split, offloaded to S3 with a pointer, or read through pagination loops that complicate every consumer.
  • It runs only on AWS and the API is proprietary rather than a standard, so moving the data layer means rewriting it; ScyllaDB's Alternator is the only meaningfully compatible target and it brings a much smaller ecosystem.

Upstash

  • HTTP-based REST API only, no TCP connections means higher latency than self-hosted or connection-pooled Redis
  • Rate limiting occurs when traffic exceeds configured budget cap or request limits
  • Kafka service discontinued in March 2025, requiring migration to Upstash Workflow or alternatives
  • Limited Redis feature support compared to self-hosted Redis or Redis Cloud
  • High-request-volume workloads can accumulate significant costs due to per-request pricing model

Pricing, plan by plan

DynamoDB

Free
  • On-Demand Capacity$null/usage-based
    • Pay-per-request pricing with automatic scaling
    • Read: 0.5 RRU per 4 KB (eventually consistent), 1 RRU per 4 KB (strongly consistent), 2 RRU per 4 KB (transactional)
    • Write: 1 WRU per 1 KB
  • Provisioned Capacity$null/hourly
    • Fixed hourly charges based on reserved capacity
    • RCU rate: $0.00013 per hour (Standard)
    • WCU rate: $0.00065 per hour (Standard)
  • Standard Table Class Storage$0.25/per GB/month
    • $0.25 per GB/month after free tier
    • First 25 GB free per month (free tier)
  • Standard-Infrequent Access Table Class$0.1/per GB/month
    • $0.10 per GB/month

Upstash

Free
  • FreeFree
    • 256 MB data
    • 500K commands per month
    • 10 GB bandwidth
  • Pay-as-you-go$0.2/per 100K commands
    • Per-request billing
    • Storage at $0.25/GB
    • Unlimited commands
  • Fixed Plan$10/month
    • 250 MB Redis
    • Predictable pricing
    • Global replication available at higher tiers

Which should you pick?

Choose DynamoDB if

  • You need managed and serverless.
  • You want to start without paying.
  • You work on AWS.
  • You also want predictable latency.
DynamoDB review

Choose Upstash if

  • You need serverless redis.
  • You want to start without paying.
  • You work on REST API, Node.js, Python, Go, Rust, Vercel Edge, Cloudflare Workers, AWS Lambda.
  • You also want serverless kafka.
Upstash review

Questions people ask

Is DynamoDB or Upstash better?
Neither clearly leads. DynamoDB starts at Free and Upstash at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DynamoDB or Upstash?
DynamoDB starts at Free and Upstash at Free.
Does DynamoDB or Upstash run on more platforms?
DynamoDB runs on AWS. Upstash runs on REST API, Node.js, Python, Go, Rust, Vercel Edge, Cloudflare Workers, AWS Lambda.
Can I use DynamoDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is DynamoDB best used for?
DynamoDB is most often used for high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known, traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise, serverless applications on lambda, where an http-based datastore avoids the connection pooling problem relational databases have, event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifier. Of those, high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known and traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise are not what Upstash is typically brought in for.
What can DynamoDB do that Upstash cannot?
DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes. Upstash covers Serverless Redis, Serverless Kafka, QStash, Global Replication.

Answered from the vendors’ own pages

DynamoDB: On-demand or provisioned capacity?

On-demand suits unpredictable or spiky traffic and removes capacity planning. Provisioned with autoscaling is considerably cheaper for steady high-volume workloads. Tables can be switched between them, though not arbitrarily often.

Upstash: Does Upstash have a free tier?

Yes. The free tier provides 256 MB of data and 500,000 commands per month with 10 GB of bandwidth in a single region, no credit card required.

Source
DynamoDB: Can I run DynamoDB outside AWS?

No. DynamoDB Local exists for development and testing only. For a production-compatible alternative elsewhere, ScyllaDB's Alternator implements the DynamoDB API, but it is a different system with a different ecosystem.

Upstash: How does Upstash work with edge platforms like Vercel and Cloudflare Workers?

Upstash uses a REST API instead of TCP connections, enabling it to work from Vercel Edge, Cloudflare Workers, and AWS Lambda without persistent connections or connection pooling.

Source
DynamoDB: Can I run ad hoc queries or analytics?

Not on the table itself. Scans are slow and expensive at scale. The usual pattern is to export to S3 or stream changes out and query them in Athena, Redshift or another analytical engine.

Upstash: What programming languages are supported?

Upstash provides native SDKs for Python, Node.js, Go, and Rust. Java, C#, and PHP developers can use the REST API or community libraries.

Source
DynamoDB: Is single-table design necessary?

It is the pattern that gets the most from DynamoDB when access patterns are well known, because it lets related items be retrieved in one query. It also makes the model harder to evolve, so many teams reasonably choose multiple simpler tables and accept extra requests.

Upstash: Is Upstash Kafka still available?

No. Upstash Kafka was deprecated on September 11, 2024 and fully discontinued on March 11, 2025. Upstash Workflow is now recommended for durable serverless messaging and task queuing.

Source
DynamoDB: What are the real limits I should design around?

400 KB per item, 1 MB per query or scan page, 100 items per transaction, roughly 3,000 read and 1,000 write units per partition, and eventual consistency on global secondary indexes.

Upstash: How is Upstash pricing structured?

Upstash uses per-request pricing at $0.20 per 100K commands for Redis, $0.25/GB for storage, and $0.40 per 100K requests for Vector database. Idle applications cost nothing.

Source
Upstash: Can Upstash be used with AWS Lambda?

Yes. Upstash works with AWS Lambda via its REST API, eliminating the need for connection pooling and making it ideal for stateless serverless functions.

Source
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